Multimodal Sentiment Analysis Integrating Text, Audio, and Video for Emotion Detection

This research proposes a novel cross-modal approach to sentiment analysis that integrates textual, audio, and visual modalities to enhance the accuracy and depth of emotion recognition. By combining textual features (sentiment words, syntactic features), audio features (tone, speech rate, pitch), and visual features (body language, facial expressions, hand gestures), the proposed system aims to capture the nuanced and multi-faceted nature of human emotions. This cross-modal approach provides a more comprehensive understanding of sentiment by considering the interplay between different modalities, leading to more accurate and informative sentiment analysis results. This research has the potential to significantly improve the accuracy and effectiveness of sentiment analysis applications across various domains, such as customer service, social media monitoring, and human-computer interaction.